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Developing and validating an algorithm to identify incident chronic dialysis patients using administrative data.

Dino Gibertoni1, Claudio Voci2, Marica Iommi3

  • 1Department of Biomedical and Neuromotor Sciences, University of Bologna, Via San Giacomo 12, 40126, Bologna, Italy. dino.gibertoni2@unibo.it.

BMC Medical Informatics and Decision Making
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PubMed
Summary

Two new algorithms accurately identify patients starting chronic dialysis using administrative healthcare data. These methods offer high sensitivity and positive predictive value, improving patient identification for research and registries.

Keywords:
Administrative dataAlgorithmAmbulatory specialty visitsCase definitionChronic dialysisHospital discharge records

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Area of Science:

  • Healthcare Informatics
  • Epidemiological Research
  • Health Services Research

Background:

  • Administrative healthcare databases are valuable for research but lack robust case definitions for chronic dialysis patients.
  • Identifying incident chronic dialysis patients is crucial for healthcare burden assessment and epidemiological studies.
  • Existing methods for identifying these patients in administrative data are limited.

Purpose of the Study:

  • To develop and validate two algorithms using administrative data for identifying incident chronic dialysis patients.
  • To compare the performance of these algorithms against a regional dialysis registry.
  • To assess the utility of administrative data for chronic dialysis patient surveillance.

Main Methods:

  • Algorithms were developed using hospital discharge records (HDR) and ambulatory specialty visits (ASV) data.
  • ICD9-CM codes for dialysis-related diagnoses and procedures were used for patient identification.
  • Validation was performed against the Emilia-Romagna regional dialysis registry for incident patients in 2014.

Main Results:

  • Algorithm 1 identified 680 patients and Algorithm 2 identified 676 incident chronic dialysis patients.
  • Sensitivities were 90.8% for Algorithm 1 and 88.4% for Algorithm 2.
  • Positive predictive values were 84.0% for Algorithm 1 and 82.0% for Algorithm 2, with high percentage agreement.

Conclusions:

  • Administrative data algorithms demonstrate high sensitivity and positive predictive value for identifying incident chronic dialysis patients.
  • Algorithm 1, with its simpler definition and higher accuracy, can substitute or enhance regional dialysis registries.
  • These algorithms improve the accuracy and timeliness of chronic dialysis patient identification in healthcare administrative databases.